Gastos Casa
27 noviembre, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 20/9/2023 | VTR | 21990 | Andrés | NA |
| 16/9/2023 | Comida | 27980 | Tami | Cajas Soul Bar |
| 23/9/2023 | Comida | 57639 | Tami | Supermercado |
| 24/9/2023 | Diosi | 8000 | Andrés | arena diosi 10kg |
| 30/9/2023 | Electricidad | 44407 | Andrés | NA |
| 30/9/2023 | Comida | 51726 | Tami | Supermercado |
| 6/10/2023 | Comida | 44298 | Tami | Supermercado |
| 14/10/2023 | Comida | 86673 | Tami | Supermercado |
| 10/10/2023 | Diosi | 6880 | Tami | Omega aceite petsu |
| 17/10/2023 | Diosi | 55990 | Tami | Comida Diosi |
| 17/10/2023 | Diosi | 50000 | Tami | Veterinaria |
| 20/10/2023 | Comida | 41970 | Tami | Barritas Wild Soul |
| 21/10/2023 | Diosi | 56170 | Andrés | n y d + 2 tarrito |
| 21/10/2023 | Comida | 50000 | Andrés | la providencia |
| 21/10/2023 | Comida | 76052 | Tami | Supermercado |
| 21/10/2023 | Diosi | 55990 | Andrés | Pelet SuperZoo |
| 26/10/2023 | Comida | 17493 | Tami | Chicken love u |
| 30/10/2023 | Comida | 61933 | Tami | Supermercado |
| 31/10/2023 | Diosi | 20000 | Tami | Veterinaria |
| 1/11/2023 | Electricidad | 44414 | Andrés | NA |
| 5/11/2023 | Comida | 47648 | Tami | Supermercado |
| 10/11/2023 | Cámaras Seguridad M.Barrios | 15000 | Tami | NA |
| 12/11/2023 | Comida | 63292 | Tami | Supermercado |
| 12/11/2023 | Gas | 78000 | Andrés | lipigas mas propina 1500 |
| 15/11/2023 | Comida | 4418 | Tami | Supermercado |
| 18/11/2023 | Comida | 53051 | Tami | Supermercado |
| 22/11/2023 | Comida | 7838 | Tami | Supermercado |
| 23/11/2023 | Comida | 31470 | Tami | Barritas Wild Soul |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.2404e+08 2 8.0204 4e-04 ***
## lag_depvar 8.8847e+10 1 1729.4955 <2e-16 ***
## Residuals 3.2775e+10 638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1070.094 13387.58 0.0164888
## 2-0 29081.138 23483.078 34679.20 0.0000000
## 2-1 21852.300 18548.610 25155.99 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 486 51315.40 15072.480
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1912.51994 3994.79299 -492.79336 2477.32398 -2880.09613 551.28976
## 8 9 10 11 12 13
## -5607.99942 -1241.72163 -4025.66563 -534.17598 -5039.33131 -1778.77610
## 14 15 16 17 18 19
## -1068.01105 223.42342 -3360.69966 -531.54025 -2261.64280 6459.25122
## 20 21 22 23 24 25
## -1523.16428 -1221.82236 1450.97787 -1170.94629 235.92529 1710.21081
## 26 27 28 29 30 31
## -7047.44243 872.77753 8154.55594 547.67035 116.79655 -2276.69492
## 32 33 34 35 36 37
## 1649.58545 4675.85934 1312.31224 2584.34022 -1644.98338 4777.89408
## 38 39 40 41 42 43
## 4404.00335 -2106.77034 -2875.35713 -1072.15416 -10728.10485 7101.70313
## 44 45 46 47 48 49
## 2529.87526 1391.37178 8152.87840 879.68314 6711.49520 6996.55747
## 50 51 52 53 54 55
## -5509.82278 -4578.66355 -4958.79763 -7933.79454 5978.36248 -4093.95088
## 56 57 58 59 60 61
## -4984.61924 3686.52143 812.51788 -80.60263 100.04750 -5029.84698
## 62 63 64 65 66 67
## 18005.29853 3873.00313 -3374.40958 6095.73527 7604.20920 15003.98953
## 68 69 70 71 72 73
## 2285.95345 -12662.21055 -1070.18536 4826.33478 -4653.10012 -4278.84191
## 74 75 76 77 78 79
## -10468.65368 2297.69086 -5500.60752 876.18851 -7009.38814 295.11211
## 80 81 82 83 84 85
## -2563.45588 -2919.21018 -4177.90983 -824.64697 2054.68057 3579.47624
## 86 87 88 89 90 91
## 387.59522 -552.93192 128.63291 4246.98289 -1130.25299 1158.65658
## 92 93 94 95 96 97
## -2035.44164 -1056.49473 148.32517 253.33508 -7496.85658 2242.35035
## 98 99 100 101 102 103
## -8687.70307 -3173.85669 -4296.85030 -2035.54714 -1553.58487 2903.24480
## 104 105 106 107 108 109
## -2524.67930 2391.86561 -1285.97954 838.35664 2490.55612 -3189.84079
## 110 111 112 113 114 115
## -4811.74575 -1014.57146 1745.07223 11591.33406 -1113.88458 2758.77198
## 116 117 118 119 120 121
## 4392.14099 3695.74484 -865.25958 -4530.76838 -3648.61488 2317.49923
## 122 123 124 125 126 127
## -1690.61514 1345.90556 8888.85922 1039.31068 315.05868 -2356.60776
## 128 129 130 131 132 133
## 2753.03702 7188.36446 1263.05743 -8260.69832 1800.79819 4213.97487
## 134 135 136 137 138 139
## -3017.69613 -1349.81817 -818.36867 -3863.86131 1125.99013 -522.46485
## 140 141 142 143 144 145
## -2945.49972 1637.06848 -1919.15332 -7896.63298 1836.09743 -3618.67722
## 146 147 148 149 150 151
## 1917.01461 -379.46880 912.44788 -436.13215 1279.18477 1148.74433
## 152 153 154 155 156 157
## 3346.30580 -4807.62857 -1216.60509 -3293.60615 5846.96094 9762.16858
## 158 159 160 161 162 163
## -3564.74400 -4946.46016 3380.13933 77.67919 2608.96647 -5925.51568
## 164 165 166 167 168 169
## -6858.62394 3946.10401 17296.32620 3868.66292 -112.45416 -2193.55818
## 170 171 172 173 174 175
## -918.32270 3742.54077 -20.44155 -7888.45524 2889.75470 4409.81258
## 176 177 178 179 180 181
## 787.18844 8912.32888 -8939.12813 -3360.10121 -10700.82528 -11387.59712
## 182 183 184 185 186 187
## 911.43448 9042.54224 -1471.52610 5875.32400 6623.96616 13343.17391
## 188 189 190 191 192 193
## 8834.00111 -3555.80267 2838.55900 10742.75791 -1128.39864 -2020.76036
## 194 195 196 197 198 199
## -9950.43794 -6253.96152 1220.99155 -5216.25817 -9868.02331 5153.32492
## 200 201 202 203 204 205
## -3165.67329 -1844.71657 -942.71310 6366.27271 9890.61341 764.03005
## 206 207 208 209 210 211
## 3101.07644 3307.91306 6026.74148 13151.73380 -5179.03488 -10948.36467
## 212 213 214 215 216 217
## -5556.82968 -10585.54872 -5253.50157 1289.18618 -13183.24328 16017.03454
## 218 219 220 221 222 223
## 7780.18754 1645.53841 26822.71664 13114.99644 8070.50415 14799.33795
## 224 225 226 227 228 229
## -2991.33121 -1002.38349 4394.60224 969.03408 3288.86454 9532.33527
## 230 231 232 233 234 235
## 6456.49994 -1248.98322 -1281.14291 9877.25635 -10942.24606 -6986.41118
## 236 237 238 239 240 241
## -8404.41373 -10126.50218 2884.36105 1244.76112 -8359.91456 -9183.27061
## 242 243 244 245 246 247
## 8775.01719 -7877.25126 2260.33956 -10448.43343 -4356.18318 1095.06574
## 248 249 250 251 252 253
## 749.10753 -12513.20112 3262.19510 1800.39686 4025.37077 2051.39454
## 254 255 256 257 258 259
## -1190.69622 11097.78493 21042.57732 3701.08060 -3775.28341 4469.87038
## 260 261 262 263 264 265
## -1306.98811 4048.29069 -4513.19695 -10679.53407 -4726.94936 -598.01233
## 266 267 268 269 270 271
## -5259.73764 8630.06700 -4255.47817 4140.03754 -2075.62047 4424.25694
## 272 273 274 275 276 277
## 781.15990 7379.64458 -1219.57177 12169.37232 -4254.90374 1933.16572
## 278 279 280 281 282 283
## -162.31332 8027.95055 -4771.19328 -2566.75322 -11162.47859 -2769.44936
## 284 285 286 287 288 289
## 18526.28620 7986.31160 3041.04896 -315.95275 1167.51712 6640.98886
## 290 291 292 293 294 295
## 7200.17506 -18381.67406 -11107.65810 -8273.24191 9405.11835 3011.44533
## 296 297 298 299 300 301
## -1176.60155 27388.98959 10506.54434 5450.25201 10076.61140 3498.85039
## 302 303 304 305 306 307
## -426.54171 8407.43723 -23720.60693 -3426.40769 -132.59168 -6927.65996
## 308 309 310 311 312 313
## -4042.61994 2814.21062 -9239.20817 -3410.61263 -8385.10370 1271.48189
## 314 315 316 317 318 319
## -3370.76476 1816.70859 -4241.61902 27250.07115 -466.28120 3505.80966
## 320 321 322 323 324 325
## 11072.01658 5974.62569 32807.11662 6003.13829 -20076.57611 2209.80434
## 326 327 328 329 330 331
## 1506.29540 -6098.64719 -1513.57020 -33097.33874 544.86082 -2562.16950
## 332 333 334 335 336 337
## -336.75844 -3362.57828 3884.46866 -530.23442 -7023.55349 -3270.13979
## 338 339 340 341 342 343
## -2356.04303 -7838.62291 3611.47765 -1502.20742 -1855.83691 -1106.29160
## 344 345 346 347 348 349
## 80.70710 417.96329 -1650.08934 -9483.53089 -13373.62754 1978.95068
## 350 351 352 353 354 355
## -4557.45230 -3911.63934 -6238.86322 1450.83772 1167.81414 2601.99119
## 356 357 358 359 360 361
## -3841.13625 -625.40652 588.73991 6960.84942 350.99629 42.81373
## 362 363 364 365 366 367
## 2665.09190 -2627.27150 -799.33114 -8674.95515 -4683.40127 -6315.39449
## 368 369 370 371 372 373
## -5115.39552 -7453.52314 4748.86802 231.80038 7019.80638 -7601.36124
## 374 375 376 377 378 379
## -2332.27125 -3465.46096 -2568.29537 -12565.15483 1639.38566 -10816.44731
## 380 381 382 383 384 385
## 5395.32280 9177.19201 3142.36154 -2324.89512 1645.52946 6816.17487
## 386 387 388 389 390 391
## 11585.01494 -5472.99739 -5159.20444 -53.96631 8659.50106 2033.77705
## 392 393 394 395 396 397
## 11444.94311 -9513.42851 2946.23449 908.62352 748.43613 -478.38755
## 398 399 400 401 402 403
## -414.72007 -14360.53299 8431.86990 -1118.11194 -1322.91223 7017.19223
## 404 405 406 407 408 409
## -7787.08127 -1274.42595 -2515.58687 -5825.90279 -2932.30396 -4003.94514
## 410 411 412 413 414 415
## -8869.24450 5925.41959 1567.59172 -7396.43116 -7803.23620 14030.31820
## 416 417 418 419 420 421
## 3871.68827 4605.59425 -7862.49852 -4698.43239 -2614.48061 2787.51908
## 422 423 424 425 426 427
## -13981.38530 -2953.10306 -9260.41012 2759.67550 6820.21772 6557.05900
## 428 429 430 431 432 433
## -3893.56845 -4082.48559 -4732.57567 -1850.16357 -5772.83690 -6745.13878
## 434 435 436 437 438 439
## -6130.83673 -1618.52406 -1044.46176 -5139.39306 2384.91252 4724.66658
## 440 441 442 443 444 445
## -5068.74953 -2225.69535 1503.86516 -3859.59460 2777.20658 -6570.03997
## 446 447 448 449 450 451
## -12182.62818 -4733.56467 9405.91095 -2077.75930 4703.46195 -5825.10633
## 452 453 454 455 456 457
## -1152.33234 360.47388 3030.47017 -12201.73758 3263.40321 -6723.77756
## 458 459 460 461 462 463
## 6422.14266 3041.28284 2594.94742 -3714.74471 2166.95336 105.89369
## 464 465 466 467 468 469
## 1908.57975 -377.02453 3484.85531 -2455.84362 5942.51203 -6718.92065
## 470 471 472 473 474 475
## -2854.96048 -2134.47592 -4614.65250 2990.32550 7856.83344 -5830.25775
## 476 477 478 479 480 481
## 1575.97133 -6057.32067 -2810.97289 2019.23382 -12872.97298 -9879.51281
## 482 483 484 485 486 487
## -1435.85160 -178.90246 -1113.40755 -1466.47251 -9692.11332 10878.53768
## 488 489 490 491 492 493
## 6235.19109 7533.70916 -5207.86983 5502.79839 9509.92904 6399.35358
## 494 495 496 497 498 499
## -13065.97665 -10396.44957 -3432.56460 -1128.88796 -539.60904 -7625.77514
## 500 501 502 503 504 505
## 518.36029 4238.79526 5552.56362 802.32730 234.08486 -7084.16291
## 506 507 508 509 510 511
## 616.05051 -4978.30197 1838.59103 -1241.88744 -8108.72129 -661.21224
## 512 513 514 515 516 517
## -2712.89955 -639.60312 1300.20487 -9481.11812 -7875.30137 24095.20185
## 518 519 520 521 522 523
## 10048.02721 6240.07471 -4915.94938 3099.76145 17341.04053 12026.73878
## 524 525 526 527 528 529
## -23480.58101 -4828.08856 -3589.84866 4659.58870 -192.82571 -10947.21247
## 530 531 532 533 534 535
## 4378.19733 13984.49178 -4683.28419 4570.66079 5809.32271 -1469.07052
## 536 537 538 539 540 541
## -4272.11421 -6891.23677 -2029.16006 8373.08137 316.49649 -7956.29473
## 542 543 544 545 546 547
## 1873.39883 -502.08060 461.90732 -10922.24736 -11113.41049 1850.24865
## 548 549 550 551 552 553
## 6891.09978 -1287.88820 863.74899 -7663.82298 8518.71767 1017.14081
## 554 555 556 557 558 559
## -11814.53433 9114.43941 8774.50809 349.45429 5089.77844 -3278.12176
## 560 561 562 563 564 565
## 14329.10260 21919.72365 -5762.78615 -9153.55836 7102.24428 628.48926
## 566 567 568 569 570 571
## 3823.22034 -6988.97806 -17064.49232 6634.19492 6532.43457 2105.07497
## 572 573 574 575 576 577
## 3323.09259 2031.77870 -1891.27433 14941.63961 -9213.32843 -5995.16112
## 578 579 580 581 582 583
## 8852.28242 3131.77906 -6246.39816 7688.52577 -3510.94071 -2563.09375
## 584 585 586 587 588 589
## 15864.96752 -14101.53218 8571.87770 342.58659 -5955.40013 -604.65211
## 590 591 592 593 594 595
## 391.62283 -10506.77293 1784.45552 -7105.17744 3016.14176 8891.21113
## 596 597 598 599 600 601
## -7322.48404 5913.61223 2899.45768 7061.23397 -2886.96899 6384.90711
## 602 603 604 605 606 607
## -7979.75995 2426.94755 1475.08317 3357.31313 1754.69210 666.96906
## 608 609 610 611 612 613
## -5548.85251 8235.48385 -898.79333 -2322.57027 -3251.35290 -8082.23653
## 614 615 616 617 618 619
## 11983.68629 5164.94278 -9017.67034 11764.52867 6371.40001 -5178.33740
## 620 621 622 623 624 625
## 26640.33056 -12151.72916 -6367.53928 3435.09873 -3845.10883 -10359.46375
## 626 627 628 629 630 631
## 11359.72802 -21381.22201 -2505.37468 8584.74795 11217.16162 -1284.03548
## 632 633 634 635 636 637
## 33517.76595 -5842.85867 6284.33320 6004.04840 -1635.89830 -4807.44193
## 638 639 640 641 642 643
## -1532.31763 -12082.95016 -2109.52723 -1780.93559 -2433.19856 -2796.43915
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17356.77 20144.21 24308.94 24032.82 26336.81 23725.42 24426.71 19758.86
## 10 11 12 13 14 15 16 17
## 19500.95 16899.46 17660.62 14458.63 14508.73 15159.43 16820.41 15175.68
## 18 19 20 21 22 23 24 25
## 16188.64 15575.32 22509.16 21612.39 21103.16 22953.52 22293.65 22932.50
## 26 27 28 29 30 31 32 33
## 24739.73 18795.51 20485.44 28158.33 28214.77 27894.55 25573.70 26946.71
## 34 35 36 37 38 39 40 41
## 30709.12 31050.23 32429.84 29992.68 34039.00 37179.77 34297.64 31175.44
## 42 43 44 45 46 47 48 49
## 30047.39 20824.58 28185.55 30570.91 31637.26 38331.89 37837.08 42401.44
## 50 51 52 53 54 55 56 57
## 46548.82 39399.95 34082.37 29209.51 22497.78 28655.81 25308.19 21683.48
## 58 59 60 61 62 63 64 65
## 25999.34 27232.46 27523.24 27926.42 23883.99 40127.14 41932.41 37278.12
## 66 67 68 69 70 71 72 73
## 41396.79 46209.30 56653.62 54709.07 40261.90 37820.09 40774.67 35194.41
## 74 75 76 77 78 79 80 81
## 30742.08 21640.59 24774.89 20786.10 22828.39 17831.03 19804.17 19046.92
## 82 83 84 85 86 87 88 89
## 18095.05 16204.50 17455.46 20987.81 25312.83 26281.93 26306.37 26910.16
## 90 91 92 93 94 95 96 97
## 30948.68 29803.77 30782.16 28887.21 28103.82 28464.24 28862.29 22574.51
## 98 99 100 101 102 103 104 105
## 25526.27 18703.00 17583.14 15664.98 15958.44 16621.61 21000.39 20103.13
## 106 107 108 109 110 111 112 113
## 23540.55 23334.93 24975.87 27792.27 25342.89 21861.00 22130.64 24721.38
## 114 115 116 117 118 119 120 121
## 35357.88 33588.66 35387.57 38322.97 40237.83 37974.77 32904.47 29322.64
## 122 123 124 125 126 127 128 129
## 31361.76 29677.81 30834.57 38274.83 37924.80 37006.04 33935.39 35679.21
## 130 131 132 133 134 135 136 137
## 40963.80 40415.84 31802.20 33040.45 36163.27 32649.25 31070.37 30174.58
## 138 139 140 141 142 143 144 145
## 26803.87 28188.61 27963.07 25697.93 27679.87 26333.49 20069.90 23036.82
## 146 147 148 149 150 151 152 153
## 20909.13 23823.75 24352.41 25909.42 26087.67 27707.11 28980.55 31949.06
## 154 155 156 157 158 159 160 161
## 27514.32 26792.75 24399.32 30169.69 41585.17 39950.46 37370.72 42285.61
## 162 163 164 165 166 167 168 169
## 43664.60 47008.80 42569.91 37975.61 43286.96 59246.91 61412.60 59859.99
## 170 171 172 173 174 175 176 177
## 56752.32 55185.17 57831.01 56875.60 49329.53 52093.76 55757.81 55793.24
## 178 179 180 181 182 183 184 185
## 62772.41 53474.10 50293.25 41294.88 33011.85 36446.46 46337.81 45805.25
## 186 187 188 189 190 191 192 193
## 51633.03 57257.40 67814.00 72985.95 66813.01 67002.38 73924.26 69691.47
## 194 195 196 197 198 199 200 201
## 65308.30 54777.96 48933.44 50327.83 46015.02 38348.25 44638.10 42902.72
## 202 203 204 205 206 207 208 209
## 42548.28 43016.58 49667.96 58370.54 58007.92 59696.52 61317.54 65029.12
## 210 211 212 213 214 215 216 217
## 74296.89 66545.94 54982.97 49704.98 40890.36 37911.96 40960.24 31189.97
## 218 219 220 221 222 223 224 225
## 47807.10 54974.18 55857.14 78144.57 85482.21 87443.38 94875.33 86016.24
## 226 227 228 229 230 231 232 233
## 80140.68 79731.39 76451.71 75630.81 80268.36 81603.98 76156.29 71469.74
## 234 235 236 237 238 239 240 241
## 77004.67 63932.84 56136.56 48256.22 40043.92 44147.81 46255.34 39843.56
## 242 243 244 245 246 247 248 249
## 33655.84 43722.39 38090.09 41943.15 34369.47 33102.51 36681.04 39445.63
## 250 251 252 253 254 255 256 257
## 30467.66 36281.03 40002.63 45088.32 47749.55 47252.79 57337.42 74467.21
## 258 259 260 261 262 263 264 265
## 74286.14 67737.27 69187.99 65488.14 66903.91 60792.68 50292.52 46403.30
## 266 267 268 269 270 271 272 273
## 46608.31 42796.79 51416.05 47767.39 51827.05 49983.17 53965.13 54254.93
## 274 275 276 277 278 279 280 281
## 60146.00 57829.91 67299.76 61352.12 61557.74 59941.48 65563.76 59425.90
## 282 283 284 285 286 287 288 289
## 56061.91 45833.59 44264.00 61134.40 66548.38 66949.24 64421.05 63527.58
## 290 291 292 293 294 295 296 297
## 67444.54 71272.67 52668.23 42978.10 37114.88 47219.55 50393.32 49525.87
## 298 299 300 301 302 303 304 305
## 73214.17 79034.75 79688.39 84204.01 82440.40 77574.99 80969.04 56394.84
## 306 307 308 309 310 311 312 313
## 52734.45 52420.95 46341.48 43609.50 47137.21 39845.76 38594.68 33270.38
## 314 315 316 317 318 319 320 321
## 36975.48 36174.01 39925.05 37951.79 63196.85 61083.33 62672.84 70503.09
## 322 323 324 325 326 327 328 329
## 72840.31 97787.15 96198.86 72536.34 71359.42 69751.22 61871.86 59054.48
## 330 331 332 333 334 335 336 337
## 29633.57 33243.74 33674.04 35945.29 35299.96 40945.95 41998.98 37346.28
## 338 339 340 341 342 343 344 345
## 36577.19 36701.19 32118.38 37991.49 38640.98 38894.01 39751.44 41499.89
## 346 347 348 349 350 351 352 353
## 43283.66 43040.53 36133.20 26898.91 32131.45 31016.35 30615.01 28281.45
## 354 355 356 357 358 359 360 361
## 32862.19 36537.72 40907.71 39134.69 40368.55 42462.15 49702.29 50241.33
## 362 363 364 365 366 367 368 369
## 50438.77 52850.27 50386.47 49842.67 42642.12 39897.68 36154.82 33980.09
## 370 371 372 373 374 375 376 377
## 30120.56 37255.63 39494.62 47214.79 41312.84 40771.60 39339.58 38882.15
## 378 379 380 381 382 383 384 385
## 29941.33 34443.02 27640.39 35687.38 45803.78 49294.47 47604.04 49553.97
## 386 387 388 389 390 391 392 393
## 55643.70 64930.28 58283.92 52868.11 52602.50 59827.37 60339.77 68826.71
## 394 395 396 397 398 399 400 401
## 58160.77 59694.81 59264.14 58758.82 57277.43 56064.96 43101.13 51506.83
## 402 403 404 405 406 407 408 409
## 50528.20 49516.09 55783.22 48482.00 47807.59 46169.33 41937.16 40792.37
## 410 411 412 413 414 415 416 417
## 38896.82 33114.72 40822.55 43687.57 38471.52 33662.68 48222.74 51986.98
## 418 419 420 421 422 423 424 425
## 55833.93 48460.86 44861.19 43564.91 47076.24 35737.96 35472.84 29851.90
## 426 427 428 429 430 431 432 433
## 35324.64 43477.80 50225.57 47058.77 44188.86 41178.45 41068.98 37620.57
## 434 435 436 437 438 439 440 441
## 33839.84 31131.81 32674.89 34485.54 32531.94 37296.19 43371.75 40192.12
## 442 443 444 445 446 447 448 449
## 39904.28 42847.74 40778.08 44684.04 40030.49 31250.56 30112.37 41231.47
## 450 451 452 453 454 455 456 457
## 40919.68 46452.53 42180.05 42522.38 44108.96 47749.31 37835.60 42583.35
## 458 459 460 461 462 463 464 465
## 38102.43 45513.00 48959.34 51525.03 48323.05 50614.82 50812.13 52522.60
## 466 467 468 469 470 471 472 473
## 52030.72 54912.84 52297.06 57242.49 50643.53 48304.48 46920.22 43615.25
## 474 475 476 477 478 479 480 481
## 47292.74 54599.83 49143.46 50811.03 45708.97 44121.91 46895.54 36531.37
## 482 483 484 485 486 487 488 489
## 30227.71 32057.90 34698.12 36156.90 37102.54 30876.46 43144.38 49665.15
## 490 491 492 493 494 495 496 497
## 56352.44 51174.63 55906.50 63380.36 67111.98 53656.02 44431.14 42497.46
## 498 499 500 501 502 503 504 505
## 42813.89 43588.49 38190.64 40539.35 45729.86 51292.53 51987.34 52095.59
## 506 507 508 509 510 511 512 513
## 45929.38 47241.30 43578.84 46276.60 45949.29 39796.64 40904.04 40096.46
## 514 515 516 517 518 519 520 521
## 41178.94 43763.69 36753.73 32131.94 55521.40 63511.21 67087.66 60605.38
## 522 523 524 525 526 527 528 529
## 61916.82 75217.98 82048.58 57523.37 52500.85 49264.41 53551.68 53068.36
## 530 531 532 533 534 535 536 537
## 43457.52 48344.79 60740.14 55375.77 58702.25 62606.50 59720.83 54855.67
## 538 539 540 541 542 543 544 545
## 48454.87 47138.92 54909.79 54665.44 47381.32 49558.37 49388.66 50067.96
## 546 547 548 549 550 551 552 553
## 40912.84 32919.61 37170.47 45117.03 44918.25 46588.39 40723.71 49547.86
## 554 555 556 557 558 559 560 561
## 50678.96 40672.27 50013.35 57711.40 57089.65 60611.98 56467.90 67981.99
## 562 563 564 565 566 567 568 569
## 84320.93 74619.56 63422.76 67749.37 65913.07 67074.84 58821.49 43146.09
## 570 571 572 573 574 575 576 577
## 50007.85 55789.21 56947.19 58979.22 59612.70 56799.36 68789.33 58385.45
## 578 579 580 581 582 583 584 585
## 52240.00 59682.22 61154.68 54393.47 60528.65 56197.52 53304.03 66589.68
## 586 587 588 589 590 591 592 593
## 52323.69 59513.98 58625.40 52479.22 51798.95 52069.20 42979.69 45717.89
## 594 595 596 597 598 599 600 601
## 40457.00 44613.79 53193.34 46664.39 52400.54 54728.48 60278.68 56517.38
## 602 603 604 605 606 607 608 609
## 61230.19 52975.62 54816.20 55576.26 57836.02 58398.03 57948.42 52247.94
## 610 611 612 613 614 615 616 617
## 59161.51 57262.28 54420.35 51195.52 44306.03 55574.91 59380.81 50506.33
## 618 619 620 621 622 623 624 625
## 60690.17 64787.34 58413.67 80175.01 65609.82 58100.04 60060.97 55511.75
## 626 627 628 629 630 631 632 633
## 46049.84 56532.65 37496.80 37359.97 46727.55 56990.32 55075.95 83202.29
## 634 635 636 637 638 639 640 641
## 73594.38 75748.95 77351.90 72188.87 65060.89 61765.81 49924.53 48327.08
## 642 643
## 47241.91 45756.01
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8252
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 8.020389 0.5348025 3.585415
## t2* 1729.495476 21.7984771 208.917044
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.446698 8.142607 15.10371
## 2 lag_depvar 1428.918880 1739.408490 2121.04099Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Nov 27 01:10:19 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Nov 27 01:10:25 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Nov 27 01:10:32 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Nov 27 01:10:38 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Nov 27 01:10:45 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Nov 27 01:10:51 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Nov 27 01:10:58 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Nov 27 01:11:04 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Nov 27 01:11:11 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Nov 27 01:11:17 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 4.7264 | 5.410333 | 5.629750 | 6.5571957 |
| Comida | 366.5578 | 310.278417 | 314.087500 | 346.0626522 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 31.9587 | 47.072333 | 38.297667 | 32.3040435 |
| Enceres | 20.2717 | 20.086417 | 17.443792 | 23.6850435 |
| Farmacia | 1.9980 | 1.831667 | 7.913875 | 8.2250870 |
| Gas/Bencina | 34.4632 | 44.325000 | 28.954333 | 27.1007826 |
| Diosi | 38.1981 | 31.180667 | 41.934250 | 39.8665000 |
| donaciones/regalos | 0.0000 | 0.000000 | 7.170083 | 5.9721522 |
| Electrodomésticos/ Mantención casa | 0.0000 | 3.944000 | 30.269500 | 18.0319130 |
| VTR | 13.1950 | 25.156667 | 22.121792 | 19.3964783 |
| Netflix | 4.6340 | 7.151583 | 7.090167 | 6.8580652 |
| Otros | 0.0000 | 3.151083 | 1.575542 | 0.8220217 |
| Total | 516.0029 | 499.588167 | 522.488250 | 534.8819348 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2169, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-12-09 00:04:58 sería de: 37.119 pesos// Percentil 95% más alto proyectado: 40.533,6
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36616.77 | 36614.10 |
| Lo.80 | 36630.72 | 36625.80 |
| Point.Forecast | 37119.18 | 38058.95 |
| Hi.80 | 39017.48 | 42768.55 |
| Hi.95 | 40061.37 | 45261.67 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2744 1010.8814
## s.e. 0.1326 29.4442
##
## sigma^2 = 27152: log likelihood = -370.86
## AIC=747.73 AICc=748.18 BIC=753.86paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2475 715.9208 9.4978
## s.e. 0.1346 288.9878 9.2492
##
## sigma^2 = 27179: log likelihood = -370.36
## AIC=748.72 AICc=749.49 BIC=756.89forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 763.0081 | 675.0316 | 750.6628 |
| Lo.80 | 878.4437 | 791.2810 | 837.7665 |
| Point.Forecast | 1096.5066 | 1010.8814 | 1030.8347 |
| Hi.80 | 1314.5695 | 1230.4817 | 1268.3966 |
| Hi.95 | 1430.0051 | 1346.7312 | 1415.5759 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.30 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.3 xts_0.13.1
## [13] forecast_8.21.1 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.3 forcats_1.0.0
## [22] dplyr_1.1.4 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
## [28] sjPlot_2.8.15 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-4 sparklyr_1.8.4 httr_1.4.7
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.1
## [37] stringi_1.8.2 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.9 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.4
## [16] askpass_1.1 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-54 colorspace_2.1-0 xfun_0.39
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-35.1
## [25] glue_1.6.2 gtable_0.3.4 emmeans_1.8.9
## [28] sjstats_0.18.2 sjmisc_2.8.9 car_3.1-2
## [31] quantmod_0.4.25 abind_1.4-5 mvtnorm_1.2-3
## [34] DBI_1.1.3 ggeffects_1.3.2 Rcpp_1.0.10
## [37] viridisLite_0.4.2 xtable_1.8-4 performance_0.10.8
## [40] bit_4.0.5 htmlwidgets_1.6.2 timeSeries_4031.107
## [43] gplots_3.1.3 ellipsis_0.3.2 spatial_7.3-14
## [46] pkgconfig_2.0.3 farver_2.1.1 nnet_7.3-16
## [49] sass_0.4.5 dbplyr_2.4.0 janitor_2.2.0
## [52] utf8_1.2.3 tidyselect_1.2.0 labeling_0.4.3
## [55] rlang_1.1.2 munsell_0.5.0 cellranger_1.1.0
## [58] tools_4.1.2 cachem_1.0.7 cli_3.6.1
## [61] generics_0.1.3 sjlabelled_1.2.0 broom_1.0.5
## [64] evaluate_0.20 fastmap_1.1.1 yaml_2.3.7
## [67] knitr_1.45 bit64_4.0.5 caTools_1.18.2
## [70] nlme_3.1-153 slam_0.1-50 xml2_1.3.3
## [73] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.14
## [76] curl_5.1.0 bslib_0.4.2 highr_0.10
## [79] fBasics_4032.96 Matrix_1.6-3 its.analysis_1.6.0
## [82] nloptr_2.0.3 urca_1.3-3 vctrs_0.6.4
## [85] pillar_1.9.0 lifecycle_1.0.3 lmtest_0.9-40
## [88] jquerylib_0.1.4 estimability_1.4.1 bitops_1.0-7
## [91] insight_0.19.7 R6_2.5.1 KernSmooth_2.23-20
## [94] janeaustenr_1.0.0 codetools_0.2-18 assertthat_0.2.1
## [97] boot_1.3-28 MASS_7.3-54 gtools_3.9.5
## [100] openssl_2.0.6 withr_2.5.2 fracdiff_1.5-2
## [103] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [106] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.6
## [109] snakecase_0.11.1 rmarkdown_2.25 carData_3.0-5
## [112] TTR_0.24.3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))